There are different approaches for constructing decision networks, such as decision trees or decision graphs, namely, a knowledge-based approach and a data-based approach. Using the knowledge-based approach, a person (known as a knowledge engineer) interviews an expert in a given field to obtain the knowledge of the expert about the field of expertise of the expert. The knowledge engineer and expert first determine the distinctions of the subject matter that are important for decision making in the field of the expert. These distinctions correspond to the variables in the domain of interest, referred to as the hypothesis. For example, if a decision graph is to be used to predict the age of a customer based on the products that customer bought in a store, there would be a variable for “age” and a variable for all relevant products. The knowledge engineer and the expert next determine the structure of the decision graph and the corresponding parameter values that quantify the conditional probability distribution.
In the data-based approach, the knowledge engineer and the expert first determine the variables of the domain. Next, data is accumulated for those variables, and an algorithm is applied that creates one or more decision graphs from this data. The accumulated data comes from real world instances of the domain or hypothesis. That is, real world instances of decision making in a given field.
Conventional decision techniques which combine knowledge-based and data-based approaches include neural networks, Bayesian Networks, Rough Set Theory, and belief networks, such as a Dempster-Shafer belief network. The Bayesian networks provide intuitive results, but are better suited to causal reasoning. Rough sets differentiate between what is certain and what is possible. Dempster-Shafer belief network is an evidential reasoning approach that relies on the Dempster-Shafer Combination Rule, which differentiates ignorance and disbelief (sometimes described as “skeptical” processing), and performs conflict resolution.
Decision makers often find it difficult to mentally combine evidence since the human tendency is to postpone risky decisions when data is incomplete, jump to conclusions, or refuse to consider conflicting data. Those versed in classical (frequentist) statistics realize that in situations where evidence is sparse, a data fusion engine is necessary. Data fusion is a difficult, unsolved problem. A contributing factor to the challenge is that the data fusion problem means many things to many people. Fusion of evidence provides a focused solution in cases where a decision maker is faced with discrete bits of information that are inherently uncertain. There are many types of data fusion, and the difference between fusion and correlation is tenuous. Therefore, improvements in fusion mechanisms and techniques are desirable.